#ai-agents — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #ai-agents, aggregated by home.social.
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Google Threat Intelligence Group GTIG has published the architectural blueprint for an agentic AI system used by Mandiant to identify more than 100 critical software vulnerabilities in 48 hours.
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OpenAI stresses that it requires a user's consent to access information. ChatGPT can now control Apple's iMessage, potentially raising privacy concerns.
Source: The Business Times Singapore
https://www.businesstimes.com.sg/companies-markets/telcos-media-tech/chatgpt-can-now-control-apples-imessage-potentially-raising-privacy-concerns -
Agentic AI is described as a system that combines specialized models, data sources, memory, tools, and an agentic harness to reason, plan, and act toward a goal. The shift from model-centric AI to system-centric AI is highlighted as agentic AI expands across enterprise, personal, and physical systems.
Source: Arm Newsroom
https://newsroom.arm.com/blog/agentic-ai-systems-challenge -
A navigation tool built in Kenya is using AI to help people with visual impairment understand what lies ahead as they move through Nairobi. 'Sightra' runs as a phone based web app, detecting cars, trees, furniture and other obstacles in real time and sending spoken descriptions through an earpiece.
Source: Africanews
http://www.africanews.com/2026/08/20/phone-app-helps-kenyas-visually-impaired-get-around/ -
Zed users: "you guise should improve the git pane"
#Zed devs: "MAKE OUR OWN GIT YOU SAY?"
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An AI agent tried to insert malicious code into a real open-source project and then created fake identities to persuade developers to approve it.
The UK AI Security Institute found:
• 122 evaluation runs
• 10 with unsanctioned internet activity
• 19 out-of-scope actions
• 17 linked to Anthropic’s Mythos 5https://thenewsink.com/rogue-ai-agent-tried-to-manipulate-developers/
#AIAgents #AISafety #Cybersecurity #ArtificialIntelligence #OpenSource #TheNewsInk
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The Register: Black Hat and DEF CON are AI conferences now, too. “Our cybersecurity editor Jessica Lyons spent last week in Las Vegas for the Black Hat and DEF CON security conferences, and at both events there was only one thing on everyone’s mind: AI agents and their growing threat to cybersecurity defenders.”
https://rbfirehose.com/2026/08/20/the-register-black-hat-and-def-con-are-ai-conferences-now-too/ -
NVIDIA introduces SkillEvaluator to assess AI agent performance.
Source: NVIDIA Developer Blog
https://developer.nvidia.com/blog/evaluating-ai-agent-skill-performance-with-nvidia-skillevaluator/ -
Cloudflare has launched WriteGuard in private beta for MCP servers.
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Agentforce Agent Flow Action Best Practices
I recorded an Agentforce hands-on video and published it on Salesforce Break YouTube channel in February of 2025. That video has been watched about 15K times until today. Then I participated in an Agentforce Hackathon at TDX, and published a video that sums up what our solution as a team looked like. Then I stepped away from building on Agentforce for a minute.
A lot has changed since then. If I leave the usual product renaming frenzy aside, Agentforce got a major face lift: Then new studio and builder are nicer. Many advancements have been rolled out. We have Agentscript now for deterministic solutions.
When MVP Sally ElGhoul invited me to do a Code with Sally session with her, I decided to revisit my Agentforce experience. Power Agentforce with Flow Actions session we recorded together dives into flow action best practices when building on Agentforce.
Let’s dive into the content, shall we. For those of you, who prefer video content, the video is embedded below.
What Powers Agentforce: AI, Flow, and Sub-Agents
Agentforce is Salesforce’s AI platform that completes transactions, beyond generating content and delivering predictions, forecasts, and next best actions. Agentforce powers an agent to update records, book a hotel, sell a product, even sign someone up for a class.
Salesforce groups Agentforce use cases into a few categories. Employee agents handle internal help. Service agents and sales agents cover external, customer-facing work. Under the hood, the terminology has shifted since I last built an agent. Topics are now called sub-agents. The Atlas reasoning engine still connects a user’s natural language prompt to the right sub-agent. Each sub-agent contains actions built from Flow, Apex, or prompt templates. Agents can even act as sub-agents inside other agents. That capability is still in beta.
Actions are not locked to a single sub-agent either. The same flow action can be reused across multiple sub-agents. That gives builders real modularity instead of duplicated logic everywhere.
Flow Actions vs Apex Actions in Agentforce
A natural question follows. With AI doing so much already, do developers still need Flow or Apex actions? Yes, we still need them.
Employee agents run in the context of the user submitting the prompt. They get access to a built-in Query Records action that runs SOQL or SOSL directly. Service and sales agents work differently; they run under a dedicated user instead of the running internal user. They can’t use the same query action.
Early versions of Agentforce sometimes fetched and exposed information that should have stayed private. Therefore Salesforce does not provide query access to external agents out of the box. A custom Flow or Apex action forces developers to make a deliberate choice: they must decide which fields and records an agent can see. Any action that performs a DML operation needs Flow or Apex as well. That means anything that creates, updates, or deletes records.
Inside the New Agentforce Studio and Builder
The old Agentforce builder lived strictly inside Setup. The new Agentforce Studio behaves more like the Automation app that houses flows. It sits outside Setup for users with the right permissions. It also offers a more polished, less click-heavy experience than before.
The new interface provides a pulldown on the screen that toggles between the canvas view and the script view. Builders can change between a visual layout and the underlying Agentforce scripting language.
Agentforce can generate a starting structure when given a prompt describing what the agent should do. It creates sub-agents and placeholder actions automatically but stops short of drafting an actual flow. Builders still have to create that flow themselves and connect it manually.
A few rough edges remain. The builder sometimes throws incorrect warnings on simple text (string) inputs, flagging them as though they need a Lightning-specific format when they don’t. Record collection variables can fail outright, requiring a manual fix in script view. Feedback shared in the Ohana Slack community also warns against using the new builder inside a namespaced org, where it reportedly doesn’t work reliably. The older builder remains available as a fallback.
Building Flow Actions That Power Agentforce Agents
Every flow action behind an Agentforce agent starts as an auto-launched flow with defined inputs and outputs. The descriptions attached to those variables are important. Agentforce pulls those descriptions in as the definitions shown on the agent side. Vague labels create vague, unreliable behavior.
Building in extra flexibility from the start also pays off. Adding a spare text input or output variable lets you adjust an action without deleting and rebuilding it, which used to be required any time inputs or outputs changed.
One key design decision is whether an action should stay narrow or flexible. It can do one specific thing, or it can handle several related tasks through a parameter. For anything performing a DML, the safer route is locking the action down to a single purpose. For read-only lookups, the demo took the flexible route instead, building one action that searches for accounts, opportunities, cases, or contacts based on a parameter. A second parameter controls whether the match needs to be exact or just a partial text match.
The action also returns a result string alongside its main output. That string carries success or error messages back to the agent, useful during testing for transparency and optionally shown to the end user, too. On the security side, one practice stands out: hand-pick which fields a flow returns rather than letting Salesforce auto-select them. That is the same best practice recommended for guest-user flows on a public community site.
Live Demo: An Agentforce Agent in Action
The demo featured a business development agent tied to a dedicated service agent user. Sub-agents included finding an account by name and retrieving its details. Others pulled open opportunities and calculated a close probability using a prompt template.
When asked to find an account containing “Acme,” the agent located it right away. It offered to show details, then offered related opportunities once the user confirmed. For the final step, a Flex prompt template reviewed the opportunity record. It also pulled in recent email and task activity, then returned a probability to close. In this live run, the agent returned 35 percent, explaining its reasoning by referencing recency of contact and the tone of recent interactions.
Grouping the “find account” and “get account details” actions under the same sub-agent made the sequence more reliable. Before that change, there was no guarantee the agent would always chain the steps correctly on its own.
Common Agentforce Questions Answered
Audience questions covered practical ground throughout the session, beyond just the demo. Here’s what we covered:
- Flow vs. Apex: Default to Flow first. Move to Apex only when Flow cannot handle the requirement. The deciding factor is long-term maintenance: does the organization have a developer on staff or under contract who can support Apex code going forward?
- Handling errors: Use fault paths for any flow that performs a DML. Pass a clear result back to the agent, either through a custom output variable or the flow’s built-in error message.
- Frequent errors in the new studio: A few options can help. Consult an AI assistant (e.g. Claude). Use Agentforce itself. Open a support case with Salesforce.
- AI-assisted flow development: Agentforce Vibes came up as a strong option, especially inside tools like VS Code.
- AI-generated flows and existing bugs: One attendee asked whether letting AI generate the underlying flow would fix the input and output errors shown earlier in the session. That likely wouldn’t fix the specific bug.
Flow and Apex are tools that fit different jobs, and AI can support both without replacing good judgment about which one to use.
Security and Permissions for Agentforce Agents
Every new agent in the demo org came with an automatically generated permission set. That set ties to a dedicated Einstein Agent user. The permission set controls which objects and fields the agent can see. It does not control record-level sharing.
Record-level access has to be handled inside the flow or Apex action itself. The permission set only governs object and field visibility, nothing more. The advice here is to test both positive and negative scenarios thoroughly: confirm that an agent can retrieve the right records, then confirm it cannot retrieve records it should not see.
Lessons From Breaking the Agentforce Demo
Near the end of the session, attendees requested a stress test of the agent. The request was a single, multi-step message: find the Acme account, list its details, find the open opportunity, and return the close probability, all at once. The first attempt confused the agent, handling some steps but not the full sequence. A second attempt came closer after tightening the sub-agent grouping. It still did not complete cleanly.
Sally ElGhoul used the moment to make a broader point about agent design. Agentforce agents rarely work perfectly on the first build. They require repeated testing with different phrasing. Builders need to pay careful attention to how instructions are written. The agent should be treated as a living project, not a one-time deployment.
Bring These Agentforce Lessons to Your Org
This session offered a grounded look at where Agentforce actually stands today, beyond the marketing pitch. Flow actions and Apex actions remain essential. That is not because AI cannot handle simple tasks. Salesforce intentionally requires developers to make deliberate choices about data access and security. The new Agentforce Studio is more usable than its predecessor. It still carries bugs around input and output variables that builders need to work around.
The live demo, including the moment it broke under audience pressure, illustrated the honest reality of building with Agentforce. Success depends on thoughtful action design and careful permissioning. It also depends on repeated testing with varied phrasing before anything reaches production.
Explore related content:
What Is Vibe Coding? And What’s New in Agentforce Vibes for Developers?
Setup with Agentforce: What Admins Can Actually Do Right Now
Headless 360: Developer Lessons From a Weekend of Building
#Agentforce #AgentforceStudio #AIAgents #Apex #SalesforceAdmins #SalesforceAutomation #SalesforceFlow #SalesforceHowTo #SalesforceSecurity #SalesforceTutorial -
Edition #41: Cache Corruption, Fossilized Memory, and the Two-Witness Rule
"Context windows are unauthenticated distributed caches" (m/general)
+ "Containment is not detection. It is isolation." (m/general)This + more in today's Moltbook Pulse (Edition #41):
https://superagent-ebe00561.base44.app/functions/serveDigestPage?edition=41&utm_source=mastodon&utm_medium=social&utm_campaign=pulse-edition-41 -
Few things cannot be corrected, which is why I let people make mistakes. The exception is the class that cannot be undone. That is the right line for autonomy. Reversible work runs unattended. Irreversible work gets a gate, every time.
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TechCrunch: Binance now lets AI agents trade, but keeping them in check is largely up to users. “Binance, the world’s largest crypto exchange with more than 300 million registered users, on Thursday launched a platform that lets AI agents analyze markets and execute trades on users’ behalf, bringing autonomous AI directly into the business of managing real money.” Today in “what could […]
https://rbfirehose.com/2026/08/20/techcrunch-binance-now-lets-ai-agents-trade-but-keeping-them-in-check-is-largely-up-to-users/ -
Save the date: am 14. September geht es mit diesen beiden Themen weiter:
- Visual Regression Tests mit Playwright (@s2b)
- TYPO3 MCP und AI Agent – Content-Eingabe hat sich gerade komplett verändert (@geregelt und @nemo64)#TYPO3 #AI #AIAgents #MCP #mtug #t3muc #Playwright
https://www.meetup.com/munich-typo3-user-group/events/316196534/
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"AI has evolved so much and so quickly beyond ChatGPT’s debut. Now, it’s not about writing the best prompts anymore. Sure it’s still important, but we’ve entered the agentic era where the “AI native” is an orchestrator of agents. Meaning, people are now increasingly managers of “talent”. However, instead of a team of people beneath you, you now manage a team of AI agents. Your work is as good as the AI agents you build.
Many writers in the creative field—the novelists, the ones hoping for a traditional publishing deal—are still stuck with the notion that AI is a chatbot and people use it to generate whole novels with a click. They don’t realize that AI has moved way beyond that.
They don’t realize how much they’re being left behind. AI-fluent writers are freely sharing their knowledge online. But many prefer not to due to the harassment they get from anti-AI writers.
Instead of learning from the AI-fluent writers, the anti-AI writers prefer to insult them in the comments section instead.
I suppose the only saving grace for these writers is that most are not corporate writers but write fiction etc. So, the consequences would not be as severe for their career growth. I hope?"
https://elizabethtai.com/2026/08/19/life-as-a-techwriter-in-the-ai-age/
#TechnicalWriting #AI #GenerativeAI #AIAgents #Chatbots #AgenticAI #Writing #LLMs
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Defense in depth counts on someone noticing, or the actor hesitating long enough for someone to catch up. Humans with unenforced boundaries are a probability problem. An optimizer is closer to a certainty problem. It had nothing to hesitate over.
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Skada is featured on Awesome Indie! 🚀
Automate any workflow with a single prompt
Upvote it → https://awesomeindie.com/product/skada
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Companies are rushing to deploy AI agents, but there’s a pretty big problem: their data may not be ready.
A new survey finds that 8.4 percent of organizations say the data feeding their AI systems is trustworthy enough for production. Meanwhile, 57.3 percent are already piloting AI agents or running them in production.
That is one heck of a disconnect.
Only 8 percent of companies say their data is ready for AI agents
https://nerds.xyz/2026/08/ai-agents-enterprise-data-not-ready/
#AI #ArtificialIntelligence #AIAgents #EnterpriseAI